This notebook contains a set of analyses for analyzing rahdo’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
rahdo | training | published before 2020 | 1,240 | 306 |
rahdo | validation | published 2020 | 109 | 40 |
rahdo | test | published after 2020 | 110 | 50 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
rahdo | Artist Klemens Franz | 5.2% | 0.3% | 16.24 |
rahdo | Renegade Game Studios | 3.2% | 0.2% | 14.26 |
rahdo | Stronghold Games | 4.4% | 0.4% | 12.25 |
rahdo | Worker Placement | 18.9% | 2.3% | 8.06 |
rahdo | City Building | 12.3% | 1.7% | 7.46 |
rahdo | Rio Grande Games | 8.6% | 1.5% | 5.78 |
rahdo | Economic | 21.2% | 6.0% | 3.52 |
rahdo | Set Collection | 31.9% | 12.1% | 2.62 |
rahdo | Dice Rolling | 24.3% | 28.6% | 0.85 |
rahdo | Take That | 4.2% | 5.2% | 0.80 |
rahdo | Miniatures Game | 2.8% | 4.9% | 0.57 |
rahdo | Abstract Strategy | 3.4% | 7.5% | 0.45 |
rahdo | Negotiation | 1.1% | 3.3% | 0.34 |
rahdo | Roll Spin And Move | 0.8% | 7.1% | 0.11 |
rahdo | Childrens Game | 0.4% | 8.4% | 0.05 |
rahdo | Wargame | 0.7% | 19.8% | 0.04 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2013 | 143693 | Glass Road | 0.998 | yes |
2 | 2010 | 70512 | Luna | 0.996 | yes |
3 | 2015 | 175878 | 504 | 0.995 | yes |
4 | 2011 | 70149 | Ora et Labora | 0.992 | yes |
5 | 2008 | 35677 | Le Havre | 0.991 | yes |
6 | 2009 | 39683 | At the Gates of Loyang | 0.991 | yes |
7 | 2016 | 177736 | A Feast for Odin | 0.990 | yes |
8 | 2010 | 66505 | The Speicherstadt | 0.989 | yes |
9 | 2019 | 283863 | The Magnificent | 0.987 | yes |
10 | 2012 | 121921 | Robinson Crusoe: Adventures on the Cursed Island | 0.987 | no |
11 | 2016 | 193558 | The Oracle of Delphi | 0.987 | yes |
12 | 2018 | 244049 | Forum Trajanum | 0.986 | yes |
13 | 2018 | 244711 | Newton | 0.985 | yes |
14 | 2016 | 192945 | Coal Baron: The Great Card Game | 0.985 | yes |
15 | 2019 | 271320 | The Castles of Burgundy | 0.985 | no |
16 | 2011 | 91873 | Strasbourg | 0.983 | yes |
17 | 2017 | 234487 | Altiplano | 0.983 | yes |
18 | 2007 | 31260 | Agricola | 0.982 | yes |
19 | 2017 | 230933 | Merlin | 0.981 | yes |
20 | 2017 | 232988 | The Castles of Burgundy: The Dice Game | 0.981 | yes |
21 | 2018 | 241831 | Reykholt | 0.980 | yes |
22 | 2016 | 200680 | Agricola (Revised Edition) | 0.980 | no |
23 | 2014 | 159675 | Fields of Arle | 0.979 | yes |
24 | 2015 | 172385 | Porta Nigra | 0.978 | yes |
25 | 2017 | 197376 | Charterstone | 0.976 | yes |
26 | 2016 | 191977 | The Castles of Burgundy: The Card Game | 0.976 | yes |
27 | 2016 | 167791 | Terraforming Mars | 0.976 | yes |
28 | 2014 | 148228 | Splendor | 0.975 | yes |
29 | 2018 | 199792 | Everdell | 0.974 | no |
30 | 2017 | 233676 | Noria | 0.972 | yes |
31 | 2014 | 159508 | AquaSphere | 0.971 | yes |
32 | 2008 | 34635 | Stone Age | 0.970 | yes |
33 | 2019 | 286096 | Tapestry | 0.969 | yes |
34 | 2012 | 122515 | Keyflower | 0.967 | yes |
35 | 2011 | 104006 | Village | 0.965 | yes |
36 | 2013 | 136888 | Bruges | 0.965 | yes |
37 | 2007 | 25554 | Notre Dame | 0.962 | yes |
38 | 2009 | 55670 | Macao | 0.962 | yes |
39 | 2012 | 123260 | Suburbia | 0.959 | yes |
40 | 2018 | 247763 | Underwater Cities | 0.958 | yes |
41 | 2014 | 157354 | Five Tribes | 0.956 | yes |
42 | 2010 | 73439 | Troyes | 0.954 | yes |
43 | 2019 | 272682 | Expedition to Newdale | 0.954 | yes |
44 | 2011 | 96848 | Mage Knight Board Game | 0.953 | yes |
45 | 2007 | 27173 | Vikings | 0.951 | yes |
46 | 2019 | 265260 | Revolution of 1828 | 0.948 | yes |
47 | 2016 | 193739 | Jórvík | 0.948 | yes |
48 | 2018 | 214887 | CO₂: Second Chance | 0.946 | yes |
49 | 2017 | 199383 | Calimala | 0.945 | yes |
50 | 2014 | 157809 | Nations: The Dice Game | 0.945 | yes |
51 | 2007 | 31594 | In the Year of the Dragon | 0.939 | yes |
52 | 2011 | 84876 | The Castles of Burgundy | 0.938 | yes |
53 | 2017 | 229220 | Santa Maria | 0.937 | yes |
54 | 2008 | 38453 | Space Alert | 0.935 | yes |
55 | 2019 | 257066 | Sierra West | 0.933 | yes |
56 | 2017 | 213984 | Notre Dame: 10th Anniversary | 0.932 | no |
57 | 2017 | 221194 | Dinosaur Island | 0.932 | yes |
58 | 2015 | 183394 | Viticulture Essential Edition | 0.931 | no |
59 | 2017 | 227789 | Heaven & Ale | 0.931 | yes |
60 | 2015 | 172386 | Mombasa | 0.931 | yes |
61 | 2015 | 172381 | My Village | 0.929 | yes |
62 | 2011 | 102680 | Trajan | 0.929 | yes |
63 | 2008 | 37380 | Roll Through the Ages: The Bronze Age | 0.926 | yes |
64 | 2017 | 201825 | Ex Libris | 0.926 | yes |
65 | 2007 | 28143 | Race for the Galaxy | 0.925 | yes |
66 | 2018 | 260428 | Pandemic: Fall of Rome | 0.925 | no |
67 | 2014 | 164928 | Orléans | 0.924 | yes |
68 | 2013 | 137408 | Amerigo | 0.924 | yes |
69 | 2019 | 283948 | Marco Polo II: In the Service of the Khan | 0.924 | no |
70 | 2013 | 143515 | Coal Baron | 0.923 | yes |
71 | 2014 | 132531 | Roll for the Galaxy | 0.923 | yes |
72 | 2013 | 144344 | Rococo | 0.922 | yes |
73 | 2017 | 220520 | Caverna: Cave vs Cave | 0.919 | yes |
74 | 2016 | 193738 | Great Western Trail | 0.916 | yes |
75 | 2018 | 231581 | AuZtralia | 0.916 | no |
76 | 2017 | 227515 | Riverboat | 0.915 | yes |
77 | 2018 | 236457 | Architects of the West Kingdom | 0.915 | yes |
78 | 2012 | 119890 | Agricola: All Creatures Big and Small | 0.915 | yes |
79 | 2019 | 270971 | Era: Medieval Age | 0.914 | yes |
80 | 2011 | 90041 | Principato | 0.910 | yes |
81 | 2017 | 229265 | Wendake | 0.908 | no |
82 | 2005 | 19857 | Glory to Rome | 0.907 | yes |
83 | 2007 | 31481 | Galaxy Trucker | 0.905 | yes |
84 | 2019 | 266507 | Clank!: Legacy – Acquisitions Incorporated | 0.905 | yes |
85 | 2006 | 22141 | Cleopatra and the Society of Architects | 0.904 | yes |
86 | 2016 | 176371 | Explorers of the North Sea | 0.903 | no |
87 | 2013 | 102794 | Caverna: The Cave Farmers | 0.903 | yes |
88 | 2017 | 220308 | Gaia Project | 0.901 | yes |
89 | 2019 | 253635 | Ragusa | 0.899 | yes |
90 | 2018 | 257966 | Passing Through Petra | 0.898 | yes |
91 | 2018 | 256570 | Crown of Emara | 0.897 | yes |
92 | 2019 | 276025 | Maracaibo | 0.895 | yes |
93 | 2009 | 45315 | Dungeon Lords | 0.894 | yes |
94 | 2019 | 264052 | Circadians: First Light | 0.893 | yes |
95 | 2012 | 119391 | Il Vecchio | 0.893 | yes |
96 | 2017 | 214000 | In the Year of the Dragon: 10th Anniversary | 0.892 | no |
97 | 2010 | 66362 | Glen More | 0.890 | yes |
98 | 2018 | 245934 | Carpe Diem | 0.889 | yes |
99 | 2018 | 240464 | Cosmic Run: Regeneration | 0.885 | yes |
100 | 2018 | 223514 | Coin & Crown | 0.883 | no |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.93 |
Decision Tree | roc_auc | binary | 0.86 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think rahdo is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2012 | 121921 | Robinson Crusoe: Adventures on the Cursed Island | 0.987 | no |
2019 | 271320 | The Castles of Burgundy | 0.985 | no |
2016 | 200680 | Agricola (Revised Edition) | 0.980 | no |
2018 | 199792 | Everdell | 0.974 | no |
2017 | 213984 | Notre Dame: 10th Anniversary | 0.932 | no |
What games does the model think rahdo is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2013 | 139771 | Star Trek: Attack Wing | 0.001 | yes |
2017 | 180845 | ELL deck | 0.005 | yes |
2005 | 12333 | Twilight Struggle | 0.006 | yes |
2011 | 93538 | Battleship Galaxies | 0.006 | yes |
1983 | 438 | Scotland Yard | 0.007 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Robinson Crusoe: Adventures on the Cursed Island | Glass Road | Fields of Arle | 504 | A Feast for Odin | Altiplano | Forum Trajanum | The Magnificent |
2 | Keyflower | Bruges | Splendor | Porta Nigra | The Oracle of Delphi | Merlin | Newton | The Castles of Burgundy |
3 | Suburbia | Amerigo | AquaSphere | Viticulture Essential Edition | Coal Baron: The Great Card Game | The Castles of Burgundy: The Dice Game | Reykholt | Tapestry |
4 | Agricola: All Creatures Big and Small | Coal Baron | Five Tribes | Mombasa | Agricola (Revised Edition) | Charterstone | Everdell | Expedition to Newdale |
5 | Il Vecchio | Rococo | Nations: The Dice Game | My Village | The Castles of Burgundy: The Card Game | Noria | Underwater Cities | Revolution of 1828 |
6 | Yedo | Caverna: The Cave Farmers | Orléans | Harbour | Terraforming Mars | Calimala | CO₂: Second Chance | Sierra West |
7 | Escape: The Curse of the Temple | Lewis & Clark: The Expedition | Roll for the Galaxy | OctoDice | Jórvík | Santa Maria | Pandemic: Fall of Rome | Marco Polo II: In the Service of the Khan |
8 | The Palaces of Carrara | Concordia | Istanbul | The Pursuit of Happiness | Great Western Trail | Notre Dame: 10th Anniversary | AuZtralia | Era: Medieval Age |
9 | Terra Mystica | Legacy: The Testament of Duke de Crecy | Subdivision | Grand Austria Hotel | Explorers of the North Sea | Dinosaur Island | Architects of the West Kingdom | Clank!: Legacy – Acquisitions Incorporated |
10 | CO₂ | City of Remnants | Roll Through the Ages: The Iron Age | Raiders of the North Sea | Agricola: Family Edition | Heaven & Ale | Passing Through Petra | Ragusa |
11 | Milestones | Sail to India | Imperial Settlers | 7 Wonders Duel | The Colonists | Ex Libris | Crown of Emara | Maracaibo |
12 | Tzolk'in: The Mayan Calendar | Asante | La Granja | The Voyages of Marco Polo | Quadropolis | Caverna: Cave vs Cave | Carpe Diem | Circadians: First Light |
13 | Space Cadets | Bremerhaven | Onirim (Second Edition) | Isle of Skye: From Chieftain to King | Citadels | Riverboat | Cosmic Run: Regeneration | Nova Luna |
14 | Targi | Tash-Kalar: Arena of Legends | Praetor | Sylvion | Arkham Horror: The Card Game | Wendake | Coin & Crown | Tiny Towns |
15 | The Great Zimbabwe | Rialto | La Isla | Automania | Aeon's End | Gaia Project | Founders of Gloomhaven | Last Bastion |
16 | Snowdonia | Spyrium | Castles of Mad King Ludwig | Queen's Architect | Honshū | In the Year of the Dragon: 10th Anniversary | New Frontiers | Masters of Renaissance: Lorenzo il Magnifico – The Card Game |
17 | Ginkgopolis | Citrus | Patchwork | Copper Country | Cottage Garden | Clans of Caledonia | Coimbra | Barrage |
18 | Tokaido | Patchistory | Port Royal | Rome: City of Marble | Black Orchestra | Kitchen Rush | Duelosaur Island | Rune Stones |
19 | Clash of Cultures | Euphoria: Build a Better Dystopia | Panamax | Arboretum | Fields of Green | Spirit Island | Concordia Venus | Hellenica: Story of Greece |
20 | Kairo | Bruxelles 1893 | Akrotiri | Lanterns: The Harvest Festival | Martians: A Story of Civilization | Valletta | Dice Settlers | Herbaceous Sprouts |
21 | Le Havre: The Inland Port | Thunderstone Advance: Numenera | The Golden Ages | Bastion | Covert | Coal Country | Grasse | Wingspan |
22 | Swordfish | Nauticus | Saint Petersburg (Second Edition) | Pirates of the 7 Seas | Star Trek: Frontiers | Gloomhaven | Tales of the Northlands: The Sagas of Noggin the Nog | Queenz: To Bee or Not to Bee |
23 | Edo | Palmyra | Medina (Second Edition) | FUSE | Foragers | Montana | The Little Flower Shop | Paladins of the West Kingdom |
24 | Archipelago | Bora Bora | Grog Island | Discoveries: The Journals of Lewis & Clark | 51st State: Master Set | Fast Forward: FLEE | Gingerbread House | The Isle of Cats |
25 | New Amsterdam | Rogue Agent | Pandemic: The Cure | Valley of the Kings: Afterlife | Tiny Epic Western | Keyper | Key Flow | Amul |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
rahdo | owned | validation | GLM | roc_auc | 0.857 |
rahdo | owned | validation | Decision Tree | roc_auc | 0.770 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 318983 | Faiyum | 0.987 | yes |
2020 | 300322 | Hallertau | 0.987 | yes |
2020 | 304420 | Bonfire | 0.982 | yes |
2020 | 184267 | On Mars | 0.982 | no |
2020 | 300327 | The Castles of Tuscany | 0.965 | yes |
2020 | 233262 | Tidal Blades: Heroes of the Reef | 0.931 | no |
2020 | 296151 | Viscounts of the West Kingdom | 0.915 | yes |
2020 | 308765 | Praga Caput Regni | 0.900 | yes |
2020 | 296100 | Rococo: Deluxe Edition | 0.880 | yes |
2020 | 306040 | Merv: The Heart of the Silk Road | 0.875 | yes |
2020 | 310442 | Feierabend | 0.869 | yes |
2020 | 267009 | Rome & Roll | 0.861 | no |
2020 | 301880 | Raiders of Scythia | 0.852 | yes |
2020 | 284742 | Honey Buzz | 0.851 | no |
2020 | 269810 | Nevada City | 0.847 | yes |
2020 | 293556 | Gloomy Graves | 0.840 | no |
2020 | 265784 | Cleopatra and the Society of Architects: Deluxe Edition | 0.835 | no |
2020 | 301716 | Glasgow | 0.830 | yes |
2020 | 312804 | Pendulum | 0.826 | yes |
2020 | 316412 | The LOOP | 0.813 | yes |
2020 | 313698 | Monster Expedition | 0.796 | no |
2020 | 302310 | Nanaki | 0.791 | no |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.766 | no |
2020 | 291508 | Tiny Epic Dinosaurs | 0.713 | no |
2020 | 298065 | Santa Monica | 0.712 | yes |
2020 | 295486 | My City | 0.706 | yes |
2020 | 282954 | Paris | 0.683 | yes |
2020 | 298371 | Wild Space | 0.682 | no |
2020 | 314040 | Pandemic Legacy: Season 0 | 0.679 | no |
2020 | 311193 | Anno 1800 | 0.679 | yes |
2020 | 312484 | Lost Ruins of Arnak | 0.675 | yes |
2020 | 320819 | Dinner in Paris | 0.674 | no |
2020 | 300001 | Renature | 0.672 | yes |
2020 | 316377 | 7 Wonders (Second Edition) | 0.660 | yes |
2020 | 317985 | Beyond the Sun | 0.658 | yes |
2020 | 293678 | Stellar | 0.653 | yes |
2020 | 279537 | The Search for Planet X | 0.639 | no |
2020 | 283155 | Calico | 0.633 | yes |
2020 | 284217 | Rush M.D. | 0.625 | yes |
2020 | 325555 | Cantaloop: Book 1 – Breaking into Prison | 0.625 | no |
2020 | 317105 | Tiny Epic Galaxies BLAST OFF! | 0.623 | no |
2020 | 306481 | Tawantinsuyu: The Inca Empire | 0.620 | yes |
2020 | 284378 | Kanban EV | 0.620 | no |
2020 | 281466 | Yedo: Deluxe Master Set | 0.618 | no |
2020 | 272739 | Clinic: Deluxe Edition | 0.609 | no |
2020 | 307844 | Atheneum: Mystic Library | 0.604 | yes |
2020 | 313349 | Indus 2500 BCE | 0.600 | yes |
2020 | 282922 | Windward | 0.586 | no |
2020 | 227224 | The Red Cathedral | 0.579 | yes |
2020 | 292333 | Cowboys II: Cowboys & Indians Edition | 0.574 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Published | ID | Name | Pr(Owned) | Owned |
2021 | 343905 | Boonlake | 0.997 | yes |
2022 | 314582 | Amsterdam | 0.993 | no |
2022 | 314580 | Hamburg | 0.990 | no |
2022 | 346645 | New York City | 0.989 | no |
2022 | 341945 | La Granja: Deluxe Master Set | 0.976 | no |
2021 | 342942 | Ark Nova | 0.951 | yes |
2022 | 349793 | Age of Rome | 0.938 | no |
2021 | 249277 | Brazil: Imperial | 0.918 | no |
2021 | 298378 | Maharaja | 0.911 | no |
2022 | 302892 | Frozen Frontier | 0.909 | no |
2023 | 331820 | Rolling Heights | 0.889 | no |
2021 | 304783 | Hadrian's Wall | 0.881 | no |
2022 | 331106 | The Witcher: Old World | 0.860 | no |
2021 | 332944 | Sobek: 2 Players | 0.858 | no |
2022 | 317511 | Tindaya | 0.857 | no |
2021 | 344277 | Corrosion | 0.853 | yes |
2021 | 329593 | Settlement | 0.845 | no |
2021 | 300523 | Biblios: Quill and Parchment | 0.841 | no |
2021 | 238799 | Messina 1347 | 0.840 | yes |
2021 | 315234 | Embarcadero | 0.835 | no |
2021 | 292899 | Tribune | 0.829 | no |
2022 | 349067 | The Lord of the Rings: The Card Game – Revised Core Set | 0.828 | no |
2021 | 339484 | Savannah Park | 0.824 | yes |
2021 | 283387 | Rocketmen | 0.824 | no |
2022 | 342810 | Marrakesh | 0.818 | no |
2022 | 330964 | Hijacked | 0.814 | no |
2022 | 342674 | Jiangnan: Life of Gentry | 0.813 | no |
2021 | 331787 | Tiny Epic Dungeons | 0.810 | no |
2022 | 311988 | Frostpunk: The Board Game | 0.791 | no |
2022 | 348070 | The Palaces of Carrara (Second Edition) | 0.791 | no |
2022 | 266018 | Trinidad | 0.789 | no |
2021 | 283242 | The Whatnot Cabinet | 0.782 | yes |
2021 | 322588 | Origins: First Builders | 0.782 | yes |
2022 | 265298 | Aquanauts | 0.777 | no |
2021 | 260524 | Beyond Humanity: Colonies | 0.776 | no |
2021 | 298069 | Cubitos | 0.773 | yes |
2021 | 277700 | Merchants Cove | 0.769 | no |
2022 | 305096 | Endless Winter: Paleoamericans | 0.766 | no |
2022 | 310873 | Carnegie | 0.765 | no |
2022 | 305462 | The Age of Atlantis | 0.760 | no |
2021 | 298102 | Roll Camera!: The Filmmaking Board Game | 0.760 | yes |
2021 | 342848 | World of Warcraft: Wrath of the Lich King | 0.759 | no |
2022 | 347703 | First Rat | 0.751 | no |
2021 | 281248 | Cape May | 0.745 | no |
2022 | 304051 | Creature Comforts | 0.733 | no |
2021 | 289550 | Lions of Lydia | 0.732 | yes |
2021 | 339789 | Welcome to the Moon | 0.732 | yes |
2021 | 341169 | Great Western Trail (Second Edition) | 0.730 | no |
2021 | 328286 | Mission ISS | 0.727 | no |
2021 | 295947 | Cascadia | 0.723 | yes |
2021 | 325698 | Juicy Fruits | 0.719 | yes |
2021 | 333553 | For the King (and Me) | 0.717 | no |
2022 | 328124 | Bot Factory | 0.717 | no |
2021 | 344768 | Mobile Markets: A Smartphone Inc. Game | 0.715 | yes |
2022 | 319807 | Shogun no Katana | 0.714 | no |
2022 | 319348 | Magna Roma | 0.702 | no |
2022 | 356033 | Libertalia: Winds of Galecrest | 0.700 | no |
2022 | 343927 | Union Stockyards | 0.694 | no |
2022 | 294702 | Tenpenny Parks | 0.693 | no |
2022 | 322565 | Silicon Valley | 0.688 | no |
2022 | 342046 | Phraya | 0.687 | no |
2021 | 339906 | The Hunger | 0.684 | yes |
2021 | 332290 | Stardew Valley: The Board Game | 0.681 | no |
2022 | 352695 | Oranienburger Kanal | 0.677 | no |
2021 | 322195 | Kokopelli | 0.676 | yes |
2021 | 316786 | Tabannusi: Builders of Ur | 0.675 | yes |
2021 | 309319 | Apogee | 0.671 | no |
2022 | 258779 | Planet Unknown | 0.667 | no |
2021 | 332386 | Brew | 0.661 | yes |
2022 | 326945 | Castles of Mad King Ludwig: Collector's Edition | 0.640 | no |
2021 | 341048 | Free Ride | 0.637 | no |
2021 | 310198 | Escape: Roll & Write | 0.636 | yes |
2021 | 299684 | Khôra: Rise of an Empire | 0.635 | yes |
2021 | 324242 | Sheepy Time | 0.631 | yes |
2022 | 323707 | MOB: Big Apple | 0.629 | no |
2022 | 311610 | Blazon | 0.628 | no |
2021 | 298383 | Golem | 0.626 | yes |
2021 | 317457 | Dinosaur World | 0.625 | no |
2021 | 302510 | Mining Colony | 0.623 | no |
2022 | 312682 | Silver Coin: Age of Monster Hunters | 0.611 | no |
2021 | 297563 | Faraway Valley | 0.611 | no |
2021 | 315937 | X-Men: Mutant Insurrection | 0.609 | yes |
2022 | 351097 | Townies | 0.600 | no |
2021 | 343526 | G.I. JOE Deck-Building Game | 0.597 | yes |
2022 | 280726 | Legacies | 0.597 | no |
2021 | 340455 | King of the Valley | 0.595 | no |
2022 | 345088 | Founders of Teotihuacan | 0.594 | yes |
2022 | 324894 | Free Radicals | 0.589 | yes |
2022 | 344620 | Pocket Master Builder | 0.588 | no |
2021 | 322622 | Floriferous | 0.585 | yes |
2021 | 314088 | Agropolis | 0.574 | no |
2021 | 306202 | Philosophia: Floating World | 0.574 | no |
2022 | 331190 | Meeples & Monsters: Kickstarter Edition | 0.569 | no |
2021 | 285036 | Shadow Kingdoms of Valeria | 0.569 | yes |
2022 | 284189 | Foundations of Rome | 0.568 | no |
2021 | 328479 | Living Forest | 0.568 | no |
2022 | 304510 | Pampero | 0.565 | no |
2021 | 328871 | Terraforming Mars: Ares Expedition | 0.557 | yes |
2022 | 317582 | Mythwind | 0.555 | no |
2021 | 330038 | Llamaland | 0.553 | no |